THE ACCURACY ANALYSIS OF FINANCIAL DISTRESS MODEL A BENCHMARK OF OPERATIONAL PERFORMANCE AND FIRMS' INVESTMENT
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This study aims to find out the difference in the level of model accuracy among the Modified Altman Prediction (Z-Score), Springate (S-Score), and Zmijewski prediction models in predicting financial distress as a model of predicting operational Management and investment performance benchmarks in Transportation sub-sector firms in the Indonesian Stock Exchange (IDX) for the 4-period time. This study is a quantitative descriptive approach. The sampling technique is purposive sampling. This study utilizes sample data from the IDX, specifically www.idx.co.id, as well as the official websites of each firm. The results demonstrate that the Modification Altman Z-Score model can predict financial distress or potential bankruptcy by correctly assigning as many as 26 out of 48 samples, achieving an accuracy rate of 54.17%. The Springate S-Score model can predict financial distress or potential bankruptcy by assigning as many as 24 samples from 48 samples with an accuracy rate of 50%. The Zmijewski model was able to predict financial distress or potential bankruptcy with the highest accuracy level among the models used in this study, achieving an accuracy rate of 70.83% on 34 out of 48 samples. The conclusion from the three model bankruptcies is that the Zmijewski model is the most suitable for firms to use if they want to attract potential investors. It is used to predict financial distress and operational performance, as well as to inform firms' investment decisions. The findings of this study suggest that additional financial distress prediction models, such as Ohlson, Grover, and others, can be utilized to compare and contrast the yields of financial distress analysis.
JEL Classification Codes: C52, G01, G32, L90, N25.
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Abdullah, K., Jannah, M., Aiman, U., Hasda, S., Fadilla, Z., Ardiawan, K. N., & Sari, M. E. (2022). Metodologi Penelitian Kuantitatif. (N. Saputra, Ed.) Pidie, Aceh: Yayasan Penerbit Muhammad Zaini.
Akintoye, A., Hardcastle, C., Beck, M., Chinyio, E., & Asenova, D. (2003). Achieving Best Value in Private Finance Initiative Project Procurement. Construction Management and Economics, 21(5), 461–470. https://doi.org/10.1080/0144619032000087285
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, XXIII(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x V
Altman, E. I., Hartzel, J., & Peck, M. (1998). Emerging Market Corporate Bonds — A Scoring System. In R. Levich, Emerging Market Capital Flows (Vol. 2, pp. 391–400). Boston, MA: Springer. https://doi.org/10.1007/978-1-4615-6197-2_25
APUS. (2024, February 21). The Four Functions of Management: How They Impact Business. Retrieved January 2025, from Business and Management Resources. Retrieved from https://www.apu.apus.edu/area-of-study/business-and-management/resources/the-four-functions-of-management/
Botti, A., Bruque-Cámara, S., Canestrino, R., Capurro, R., Celenta, R., Cirillo, A., . . . Żabiński, M. (2021). Doing Business Digitally Textbook. (P. Magliocca, Ed.) Cracow, Poland: Małopolska School of Public Administration, Cracow University of Economics.
Elloumi, F., & Gueyié, J.‐P. (2001). Financial Distress and Corporate Governance: An Empirical Analysis. Corporate Governance, 1(1), 15–23. https://doi.org/10.1108/14720700110389548
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management–Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, 121598. https://doi.org/10.1016/j.techfore.2022.121598
Galdi, P., & Tagliaferri, R. (2018). Data mining: accuracy and error measures for classification and prediction. Encyclopedia of bioinformatics and computational biology, 1, 431–436. https://doi.org/10.1016/B978-0-12-809633-8.20474-3
Handoko, T. H. (2019). Manajemen Produksi dan Operasi. Yogyakarta: BPFE.
Hartanto. (1984). Analisis Laporan Keuangan. Yogayakarta: BPFE.
Husain, T. (2019). An analysis of modeling audit quality measurement based on decision support systems (DSS). Measurement, 275, 310-326.
ICAI. (2021). Operations Management & Strategic Management: Study Notes (Intermediate: PAPER-9). Directorate of Studies. Kolkata: The Institute of Cost Accountants of India.
Kothari, A. (2025, January 29). What is Operations Management? Retrieved February 2025, from Tallyfy, Inc.: Retrieved from https://tallyfy.com/guides/operations-management/
Lau, A. (1987). A Five-State Financial Distress Prediction Model. Journal of Accounting Research, 25(Spring), 127–138.
Meilawati, A., Damayanti, & Ilhami, S. D. (2023). Comparative Analysis of Altman, Springate, and Zmijewski Models in Predicting Bankruptcy in Hotel, Restaurant, and Tourism Subsector Companies for the Period 2017-2021 (in Indonesian Version). Jurnal Ilmiah FEASIBLE (JIF): Bisnis, Kewirausahaan dan Koperasi, 5(2), 155-162. https://doi.org/10.32493/fb.v5i2.2023.163-172.33302
Mentariningrum, R. A., & Prasetiono. (2022). The Influence of Variables from Zmijewski Analysis on Stock Returns in Transportation Sub-Sector Service Companies during the Pandemic (in Indonesian Version). Diponegoro Journal of Management, 11(1), 1–15.
Myers, S. C., & Pogue, G. A. (1974). A Programming Approach to Corporate Financial Management. The Journal of Finance, 29(2), 579–599. https://doi.org/10.2307/2978829
Nasution, S. W. P. (2023). Manajemen Keuangan Dasar (Cetakan 1). Yogyakarta: Deepublish.
Platt, H. D., & Platt, M. B. (2002). Predicting Corporate Financial Distress: Reflections on Choice-Based Sample Bias. Journal of Economics and Finance, 26, 184–199. https://doi.org/10.1007/BF02755985
Porter, A. (2011). Operations Management. Albert Porter & Ventus Publishing ApS.
Ramli, R. R., & Jatmiko, B. P. (2020, Agustus 7). Sektor Transportasi Diperkirakan Belum Bisa Pulih hingga Tahun Depan. Retrieved from https://money.kompas.com/read/2020/08/07/153026326/sektor-transportasi-diperkirakan-belum-bisa-pulih-hingga-tahun-depan
Robiansyah, A., Yusmaniarti, Sari, I. K., Novrianda, H., & Irwanto, T. (2022). Comparative Analysis of Altman, Springate, Zmijewski, and Grover Models in Predicting Bankruptcy of Firms on the Indonesia Stock Exchange (Empirical Study on Manufacturing Firms Listed on the IDX from 2012-2017) (in Indonesian Version). Ekombis Review – Jurnal Ilmiah Ekonomi dan Bisnis, 10(S1), 25-36. https://doi.org/10.37676/ekombis.v10iS1
Springate, G. L. (1978). Predicting the Possibility of Failure in a Canadian Firm. Theses (Faculty of Business Administration). Simon Fraser University.
Sugiyono. (2023). Metode Penelitian Kombinasi (Mixed Methods) dengan 9 Desain (2nd Ed., Vol. 2). Bandung: Alfabeta.
Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting Financial Distress and Corporate Failure: A Review from the State-of-the-Art Definitions, Modeling, Sampling, and Featuring Approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006
Supply Chain Indonesia. (2024, Maret 4). Sektor Logistik dan Transportasi Terus Tumbuh di 2024. Retrieved January 2025, from News: Retrieved from https://supplychainindonesia.com/sektor-logistik-dan-transportasi-terus-tumbuh-di-2024/
Teng, M. (2002). Corporate Turnaround: Merawat Perusahaan Sakit Menjadi Sehat Kembali (2nd Ed.). (B. Muhammad, Trans.) Jakarta: Prenhallindo.
Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Studies on Current Econometric Issues in Accounting, 22, 59-82. https://doi.org/10.2307/249085